1. Field of the Invention
The present invention relates generally to the field of computer imaging, and, more particularly, to using salient features of an image to determine seeds for detecting objects.
2. Description of the Related Art
Detecting object shapes in a two-dimensional (“2D”) and three-dimensional (“3D”) image is essential in a number of applications, such as computer-aided detection and diagnosis. A computer-aided detection and diagnosis application, for example, typically uses shape localization as a preliminary step for identifying specific structures that may be of interest (e.g., potentially indicative of disease). The term “shape localization” refers to associating coordinates to a given position in a volume or space.
Shape localization may typically proceed in two steps:
(1) Identification and extraction of collections (i.e., regions) of pixels/voxels that collectively or individually characterize a shape; and
(2) Evaluation and analysis of the collections using various shape descriptors/metrics to determine whether the collections adequately represent the shape in consideration.
Approaches for region determination include, but are not limited to, region growing, region clustering and region segmentation. Traditional region growing techniques, such as greedy region, may have very simple criteria to select seeds (i.e., starting points) for growing a region. For example, one exemplary region growing technique may consider every pixel/voxel in an image and verify whether the region extracted from a particular pixel/voxel possesses desired characteristics (e.g., compactness, ellipsoidal structure or others), which are representative of shape features associated with a desired shape. If the extracted region possesses the desired characteristics, then the extracted region can be considered an “instance” of the detected shape.
As used herein, the term “shape” refers to a space or volume surrounded by a boundary that separates the space or volume from adjacent material or structures. Such boundaries may have a sharp or a fuzzy transition (i.e., edge). A boundary is a special type of transition that has a definite extent in the direction perpendicular to the transition. The quality of the transition varies depending on the material and the imaging method used in acquiring the data. For one example, the edge may be binary (e.g., a direct transition from black to white or vice versa) if acquired with a laser-range scanner imaging a surface. For another example, the edge may be sharp with an intensity transition, such as in the case of computer tomographic (“CT”) or x-ray images of materials (e.g., suitcases) or of persons undergoing routine physical examinations. For yet another example, the edge may not be well-defined locally as in the case of ultrasound or magnetic resonance imaging. However, irrespective of the above-described quality of a boundary, any point on the boundary that determines the separation of a desired structure from undesired, neighboring structures can be used as a seed to grow a region.
Referring now to FIG. 1, an exemplary computer tomography (“CT”) image of a portion of a colon 100 is shown boundary 105 (i.e., white-ribbon area) which is a transition between two regions: the lumen 110 (i.e., the shaded area) and separating tissue 115 (i.e., the patterned area). The area enclosed by the dashed line 120 illustrates an example of a protrusion (i.e., convex region) that one may desire to detect. This convex region may also be referred to as a region of interest. In the context of the CT image of the colon 100, the area enclosed by the dashed line 120 may be, for example, a colonic polyp or a pulmonary nodule attached to the pleura.
Referring now to FIG. 2, another view of the CT image of the portion of the colon 100 of FIG. 1 is illustrated. FIG. 2 more clearly illustrates a convex region 205 and a virtual surface 210 that segments the convex region 205. The virtual surface 210 is a smooth continuation of the boundary 215 if the protrusion (i.e., the convex region 205) did not exist. It should be noted that the virtual surface 210 is shown in FIG. 2 only as a visual aid.
In a traditional greedy algorithm, all surface points on the boundary 105 may be considered as potential seeds. Such a process may be unduly time-consuming and inefficient, especially in large (e.g., on the order of several million pixels/voxels) images.